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Data analytics to reduce stop-on-fail test in electronics manufacturing

Data analytics to reduce stop-on-fail test in electronics manufacturing

Hinojosa Herrera, Ana Elsa ORCID: 0000-0002-0636-1881, Stoyanov, Stoyan ORCID: 0000-0001-6091-1226, Bailey, Christopher ORCID: 0000-0002-9438-3879, Walshaw, Christopher ORCID: 0000-0003-0253-7779 and Yin, Chunyan ORCID: 0000-0003-0298-0420 (2019) Data analytics to reduce stop-on-fail test in electronics manufacturing. Open Computer Sciences, 9 (1). pp. 200-211. ISSN 2299-1093 (Online) (doi:https://doi.org/10.1515/comp-2019-0014)

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Abstract

The use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or a combination of both have been used to improve processes in electronic manufacturing. This paper presents the application of classical techniques to reduce production cycle time by compacting a production test sequence. This set of tests is run on stop-on-fail scenario for quality assurance of an electronical device. Data generated in the production test-set on stop-on-fail scenario challenges the traditional application of the data driven techniques, because of the missing data characteristic. The developed computational procedures handle this application-specific data attribute. The novelty of this work is in the algorithm developed, which applies classical techniques in an iterative environment, as a strategy to analyse incomplete datasets. Results show that the method can reduce a production test set with parametric and non-parametric tests by building an accurate prognostic model. The results can reduce production cycle time and costs. The paper details and provides discussions on the advantages and limitations of the proposed algorithms.

Item Type: Article
Uncontrolled Keywords: decision tree, logistic regression, random forest, incomplete dataset, electronic device
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG)
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 19 Aug 2019 14:56
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24940

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